Neural networks programming with prorealtime

• 09/02/2018 at 9:21 PM #79593
  Leo

  Hi all here is my last version.

  It store the last 5 inputs data and improve the algorithm over this information, we prioritise more the most recent data.

  // Hyperparameters to be optimized // ETA=1 //known as the learning rate // candlesback=7 // for the classifier //ProfitRiskRatio=2 // for the classifier //spread=0.9 // for the classifier ///////////////// CLASSIFIER ///////////// myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT ///////////////////////// NEURONAL NETWORK /////////////////// // ...INITIAL VALUES... once a11=1 once a12=1 once a13=1 once a14=1 once a21=1 once a22=1 once a23=1 once a24=1 once a31=1 once a32=1 once a33=1 once a34=1 once a41=1 once a42=1 once a43=1 once a44=1 once a51=1 once a52=1 once a53=1 once a54=1 once a61=1 once a62=1 once a63=1 once a64=1 once Fbias1=0 once Fbias2=0 once Fbias3=0 once Fbias4=0 once Fbias5=0 once Fbias6=0 once b11=1 once b12=1 once b13=1 once b14=1 once b15=1 once b16=1 once b21=1 once b22=1 once b23=1 once b24=1 once b25=1 once b26=1 once Obias1=0 once Obias2=0 // ...DEFINITION OF INPUTS... SMA20=average[min(20,barindex)](close) SMA200=average[min(200,barindex)](close) SMA2400=average[min(2400,barindex)](close) //in 5 min time frame this is the value of SMA 200 periods in hourly variable1= RSI[14](close) // or to be defined variable2= (close-SMA20)/SMA20 *100 //or to be defined variable3= (SMA20-SMA200)/SMA200 *100 //or to be defined variable4= (SMA200-SMA2400)/SMA2400 *100 // to be defined // >>> LEARNING PROCESS <<< // If the classifier has detected a wining trade in the past //IF hour > 7 and hour < 21 then //STORING THE LEARNING DATA IF classifierlong=1 or classifiershort=1 THEN BBBBBcandleentry=BBBBcandleentry BBBBBY1=BBBBY1 BBBBBY2=BBBBY2 BBBBcandleentry=BBBcandleentry BBBBY1=BBBY1 BBBBY2=BBBY2 BBBcandleentry=BBcandleentry BBBY1=BBY1 BBBY2=BBY2 BBcandleentry=Bcandleentry BBY1=BY1 BBY2=BY2 Bcandleentry=max(candleentrylong,candleentryshort) BY1=classifierlong BY2=classifiershort ENDIF IF BARINDEX > 2500 THEN IF classifierlong=1 or classifiershort=1 THEN IF hour > 8 and hour < 21 then FOR i=1 to 5 DO // THIS HAVE TO BE IMPROVED IF i = 1 THEN candleentry=BBBBBcandleentry Y1=BBBBBY1 Y2=BBBBBY2 ENDIF IF i = 2 THEN candleentry=BBBBcandleentry Y1=BBBBY1 Y2=BBBBY2 ENDIF IF i = 3 THEN candleentry=BBBcandleentry Y1=BBBY1 Y2=BBBY2 ENDIF IF i = 4 THEN candleentry=BBcandleentry Y1=BBY1 Y2=BBY2 ENDIF IF i = 5 THEN candleentry=Bcandleentry Y1=BY1 Y2=BY2 ENDIF // >>> INPUT FOR NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] ETAi=(ETA/5)*i //Learning Rate // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) // >>> PARTIAL DERIVATES OF COST FUNCTION <<< // ... CROSS-ENTROPY AS COST FUCTION ... // COST = - ( (Y1*LOG(output1)+(1-Y1)*LOG(1-output1) ) - (Y2*LOG(output2)+(1-Y2)*LOG(1-output2) ) DerObias1 = (output1-Y1) * 1 DerObias2 = (output2-Y2) * 1 Derb11 = (output1-Y1) * F1 Derb12 = (output1-Y1) * F2 Derb13 = (output1-Y1) * F3 Derb14 = (output1-Y1) * F4 Derb15 = (output1-Y1) * F5 Derb16 = (output1-Y1) * F6 Derb21 = (output2-Y2) * F1 Derb22 = (output2-Y2) * F2 Derb23 = (output2-Y2) * F3 Derb24 = (output2-Y2) * F4 Derb25 = (output2-Y2) * F5 Derb26 = (output2-Y2) * F6 //Implementing BackPropagation Obias1=Obias1-ETAi*DerObias1 Obias2=Obias2-ETAi*DerObias2 b11=b11-ETAi*Derb11 b12=b12-ETAi*Derb12 b13=b11-ETAi*Derb13 b14=b11-ETAi*Derb14 b15=b11-ETAi*Derb15 b16=b11-ETAi*Derb16 b21=b11-ETAi*Derb21 b22=b12-ETAi*Derb22 b23=b11-ETAi*Derb23 b24=b11-ETAi*Derb24 b25=b11-ETAi*Derb25 b26=b11-ETAi*Derb26 // >>> PARTIAL DERIVATES OF COST FUNCTION (LAYER) <<< DerFbias1 = (output1-Y1) * b11 * F1*(1-F1) * 1 + (output2-Y2) * b21 * F1*(1-F1) * 1 DerFbias2 = (output1-Y1) * b12 * F2*(1-F2) * 1 + (output2-Y2) * b22 * F2*(1-F2) * 1 DerFbias3 = (output1-Y1) * b13 * F3*(1-F3) * 1 + (output2-Y2) * b23 * F3*(1-F3) * 1 DerFbias4 = (output1-Y1) * b14 * F4*(1-F4) * 1 + (output2-Y2) * b24 * F4*(1-F4) * 1 DerFbias5 = (output1-Y1) * b15 * F5*(1-F5) * 1 + (output2-Y2) * b25 * F5*(1-F5) * 1 DerFbias6 = (output1-Y1) * b16 * F6*(1-F6) * 1 + (output2-Y2) * b26 * F6*(1-F6) * 1 Dera11 = (output1-Y1) * b11 * F1*(1-F1) * input1 + (output2-Y2) * b21 * F1*(1-F1) * input1 Dera12 = (output1-Y1) * b11 * F1*(1-F1) * input2 + (output2-Y2) * b21 * F1*(1-F1) * input2 Dera13 = (output1-Y1) * b11 * F1*(1-F1) * input3 + (output2-Y2) * b21 * F1*(1-F1) * input3 Dera14 = (output1-Y1) * b11 * F1*(1-F1) * input4 + (output2-Y2) * b21 * F1*(1-F1) * input4 Dera21 = (output1-Y1) * b12 * F2*(1-F2) * input1 + (output2-Y2) * b22 * F2*(1-F2) * input1 Dera22 = (output1-Y1) * b12 * F2*(1-F2) * input2 + (output2-Y2) * b22 * F2*(1-F2) * input2 Dera23 = (output1-Y1) * b12 * F2*(1-F2) * input3 + (output2-Y2) * b22 * F2*(1-F2) * input3 Dera24 = (output1-Y1) * b12 * F2*(1-F2) * input4 + (output2-Y2) * b22 * F2*(1-F2) * input4 Dera31 = (output1-Y1) * b13 * F3*(1-F3) * input1 + (output2-Y2) * b23 * F3*(1-F3) * input1 Dera32 = (output1-Y1) * b13 * F3*(1-F3) * input2 + (output2-Y2) * b23 * F3*(1-F3) * input2 Dera33 = (output1-Y1) * b13 * F3*(1-F3) * input3 + (output2-Y2) * b23 * F3*(1-F3) * input3 Dera34 = (output1-Y1) * b13 * F3*(1-F3) * input4 + (output2-Y2) * b23 * F3*(1-F3) * input4 Dera41 = (output1-Y1) * b14 * F4*(1-F4) * input1 + (output2-Y2) * b24 * F4*(1-F4) * input1 Dera42 = (output1-Y1) * b14 * F4*(1-F4) * input2 + (output2-Y2) * b24 * F4*(1-F4) * input2 Dera43 = (output1-Y1) * b14 * F4*(1-F4) * input3 + (output2-Y2) * b24 * F4*(1-F4) * input3 Dera44 = (output1-Y1) * b14 * F4*(1-F4) * input4 + (output2-Y2) * b24 * F4*(1-F4) * input4 Dera51 = (output1-Y1) * b15 * F5*(1-F5) * input1 + (output2-Y2) * b25 * F5*(1-F5) * input1 Dera52 = (output1-Y1) * b15 * F5*(1-F5) * input2 + (output2-Y2) * b25 * F5*(1-F5) * input2 Dera53 = (output1-Y1) * b15 * F5*(1-F5) * input3 + (output2-Y2) * b25 * F5*(1-F5) * input3 Dera54 = (output1-Y1) * b15 * F5*(1-F5) * input4 + (output2-Y2) * b25 * F5*(1-F5) * input4 Dera61 = (output1-Y1) * b16 * F6*(1-F6) * input1 + (output2-Y2) * b26 * F6*(1-F6) * input1 Dera62 = (output1-Y1) * b16 * F6*(1-F6) * input2 + (output2-Y2) * b26 * F6*(1-F6) * input2 Dera63 = (output1-Y1) * b16 * F6*(1-F6) * input3 + (output2-Y2) * b26 * F6*(1-F6) * input3 Dera64 = (output1-Y1) * b16 * F6*(1-F6) * input4 + (output2-Y2) * b26 * F6*(1-F6) * input4 //Implementing BackPropagation Fbias1=Fbias1-ETAi*DerFbias1 Fbias2=Fbias2-ETAi*DerFbias2 Fbias3=Fbias3-ETAi*DerFbias3 Fbias4=Fbias4-ETAi*DerFbias4 Fbias5=Fbias5-ETAi*DerFbias5 Fbias6=Fbias6-ETAi*DerFbias6 a11=a11-ETAi*Dera11 a12=a12-ETAi*Dera12 a13=a13-ETAi*Dera13 a14=a14-ETAi*Dera14 a21=a21-ETAi*Dera21 a22=a22-ETAi*Dera22 a23=a23-ETAi*Dera23 a24=a24-ETAi*Dera24 a31=a31-ETAi*Dera31 a32=a32-ETAi*Dera32 a33=a33-ETAi*Dera33 a34=a34-ETAi*Dera34 a41=a41-ETAi*Dera41 a42=a42-ETAi*Dera42 a43=a43-ETAi*Dera43 a44=a44-ETAi*Dera44 a51=a51-ETAi*Dera51 a52=a52-ETAi*Dera52 a53=a53-ETAi*Dera53 a54=a54-ETAi*Dera54 a61=a61-ETAi*Dera61 a62=a62-ETAi*Dera62 a63=a63-ETAi*Dera63 a64=a64-ETAi*Dera64 //GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64) NEXT ENDIF ENDIF //ENDIF /////////////////// NEW PREDICTION /////////////////// // >>> INPUT NEURONS <<< input1=variable1 input2=variable2 input3=variable3 input4=variable4 // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) ENDIF return output1 coloured(0,150,0) style(line,2) as "prediction long" , output2 coloured(200,0,0) style(line,2) as "prediction short",0.5 coloured(0,0,200) as "0.5", 0.6 coloured(0,0,200) as "0.6", 0.7 coloured(0,0,200) as "0.7", 0.8 coloured(0,0,200) as "0.8"

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  // Hyperparameters to be optimized   // ETA=1 //known as the learning rate // candlesback=7 // for the classifier //ProfitRiskRatio=2 // for the classifier //spread=0.9 // for the classifier     /////////////////    CLASSIFIER    /////////////   myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize     //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT   //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT   /////////////////////////  NEURONAL NETWORK  ///////////////////   // ...INITIAL VALUES... once a11=1 once a12=1 once a13=1 once a14=1   once a21=1 once a22=1 once a23=1 once a24=1   once a31=1 once a32=1 once a33=1 once a34=1   once a41=1 once a42=1 once a43=1 once a44=1   once a51=1 once a52=1 once a53=1 once a54=1   once a61=1 once a62=1 once a63=1 once a64=1   once Fbias1=0 once Fbias2=0 once Fbias3=0 once Fbias4=0 once Fbias5=0 once Fbias6=0   once b11=1 once b12=1 once b13=1 once b14=1 once b15=1 once b16=1   once b21=1 once b22=1 once b23=1 once b24=1 once b25=1 once b26=1   once Obias1=0 once Obias2=0   // ...DEFINITION OF INPUTS...   SMA20=average[min(20,barindex)](close) SMA200=average[min(200,barindex)](close) SMA2400=average[min(2400,barindex)](close) //in 5 min time frame this is the value of SMA 200 periods in hourly   variable1= RSI[14](close)     // or to be defined variable2= (close-SMA20)/SMA20 *100 //or to be defined variable3= (SMA20-SMA200)/SMA200 *100 //or to be defined variable4= (SMA200-SMA2400)/SMA2400 *100   // to be defined   //   >>>    LEARNING PROCESS  <<< // If the classifier has detected a wining trade in the past //IF hour > 7 and hour < 21 then   //STORING THE LEARNING DATA IF classifierlong=1 or classifiershort=1 THEN BBBBBcandleentry=BBBBcandleentry BBBBBY1=BBBBY1 BBBBBY2=BBBBY2 BBBBcandleentry=BBBcandleentry BBBBY1=BBBY1 BBBBY2=BBBY2 BBBcandleentry=BBcandleentry BBBY1=BBY1 BBBY2=BBY2 BBcandleentry=Bcandleentry BBY1=BY1 BBY2=BY2 Bcandleentry=max(candleentrylong,candleentryshort) BY1=classifierlong BY2=classifiershort ENDIF   IF BARINDEX > 2500 THEN IF classifierlong=1 or classifiershort=1 THEN IF hour > 8 and hour < 21 then FOR i=1 to 5 DO //  THIS HAVE TO BE IMPROVED IF i = 1 THEN candleentry=BBBBBcandleentry Y1=BBBBBY1 Y2=BBBBBY2 ENDIF IF i = 2 THEN candleentry=BBBBcandleentry Y1=BBBBY1 Y2=BBBBY2 ENDIF IF i = 3 THEN candleentry=BBBcandleentry Y1=BBBY1 Y2=BBBY2 ENDIF IF i = 4 THEN candleentry=BBcandleentry Y1=BBY1 Y2=BBY2 ENDIF IF i = 5 THEN candleentry=Bcandleentry Y1=BY1 Y2=BY2 ENDIF   //    >>> INPUT FOR NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry]     ETAi=(ETA/5)*i //Learning Rate //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2))   //   >>> PARTIAL DERIVATES OF COST FUNCTION <<< //     ... CROSS-ENTROPY AS COST FUCTION ... // COST = - ( (Y1*LOG(output1)+(1-Y1)*LOG(1-output1) ) - (Y2*LOG(output2)+(1-Y2)*LOG(1-output2) )   DerObias1 = (output1-Y1) * 1 DerObias2 = (output2-Y2) * 1   Derb11    = (output1-Y1) * F1 Derb12    = (output1-Y1) * F2 Derb13    = (output1-Y1) * F3 Derb14    = (output1-Y1) * F4 Derb15    = (output1-Y1) * F5 Derb16    = (output1-Y1) * F6   Derb21    = (output2-Y2) * F1 Derb22    = (output2-Y2) * F2 Derb23    = (output2-Y2) * F3 Derb24    = (output2-Y2) * F4 Derb25    = (output2-Y2) * F5 Derb26    = (output2-Y2) * F6   //Implementing BackPropagation Obias1=Obias1-ETAi*DerObias1 Obias2=Obias2-ETAi*DerObias2   b11=b11-ETAi*Derb11 b12=b12-ETAi*Derb12 b13=b11-ETAi*Derb13 b14=b11-ETAi*Derb14 b15=b11-ETAi*Derb15 b16=b11-ETAi*Derb16   b21=b11-ETAi*Derb21 b22=b12-ETAi*Derb22 b23=b11-ETAi*Derb23 b24=b11-ETAi*Derb24 b25=b11-ETAi*Derb25 b26=b11-ETAi*Derb26   //   >>> PARTIAL DERIVATES OF COST FUNCTION (LAYER) <<<   DerFbias1 = (output1-Y1) * b11 * F1*(1-F1) * 1 + (output2-Y2) * b21 * F1*(1-F1) * 1 DerFbias2 = (output1-Y1) * b12 * F2*(1-F2) * 1 + (output2-Y2) * b22 * F2*(1-F2) * 1 DerFbias3 = (output1-Y1) * b13 * F3*(1-F3) * 1 + (output2-Y2) * b23 * F3*(1-F3) * 1 DerFbias4 = (output1-Y1) * b14 * F4*(1-F4) * 1 + (output2-Y2) * b24 * F4*(1-F4) * 1 DerFbias5 = (output1-Y1) * b15 * F5*(1-F5) * 1 + (output2-Y2) * b25 * F5*(1-F5) * 1 DerFbias6 = (output1-Y1) * b16 * F6*(1-F6) * 1 + (output2-Y2) * b26 * F6*(1-F6) * 1   Dera11    = (output1-Y1) * b11 * F1*(1-F1) * input1 + (output2-Y2) * b21 * F1*(1-F1) * input1 Dera12    = (output1-Y1) * b11 * F1*(1-F1) * input2 + (output2-Y2) * b21 * F1*(1-F1) * input2 Dera13    = (output1-Y1) * b11 * F1*(1-F1) * input3 + (output2-Y2) * b21 * F1*(1-F1) * input3 Dera14    = (output1-Y1) * b11 * F1*(1-F1) * input4 + (output2-Y2) * b21 * F1*(1-F1) * input4   Dera21    = (output1-Y1) * b12 * F2*(1-F2) * input1 + (output2-Y2) * b22 * F2*(1-F2) * input1 Dera22    = (output1-Y1) * b12 * F2*(1-F2) * input2 + (output2-Y2) * b22 * F2*(1-F2) * input2 Dera23    = (output1-Y1) * b12 * F2*(1-F2) * input3 + (output2-Y2) * b22 * F2*(1-F2) * input3 Dera24    = (output1-Y1) * b12 * F2*(1-F2) * input4 + (output2-Y2) * b22 * F2*(1-F2) * input4   Dera31    = (output1-Y1) * b13 * F3*(1-F3) * input1 + (output2-Y2) * b23 * F3*(1-F3) * input1 Dera32    = (output1-Y1) * b13 * F3*(1-F3) * input2 + (output2-Y2) * b23 * F3*(1-F3) * input2 Dera33    = (output1-Y1) * b13 * F3*(1-F3) * input3 + (output2-Y2) * b23 * F3*(1-F3) * input3 Dera34    = (output1-Y1) * b13 * F3*(1-F3) * input4 + (output2-Y2) * b23 * F3*(1-F3) * input4   Dera41    = (output1-Y1) * b14 * F4*(1-F4) * input1 + (output2-Y2) * b24 * F4*(1-F4) * input1 Dera42    = (output1-Y1) * b14 * F4*(1-F4) * input2 + (output2-Y2) * b24 * F4*(1-F4) * input2 Dera43    = (output1-Y1) * b14 * F4*(1-F4) * input3 + (output2-Y2) * b24 * F4*(1-F4) * input3 Dera44    = (output1-Y1) * b14 * F4*(1-F4) * input4 + (output2-Y2) * b24 * F4*(1-F4) * input4   Dera51    = (output1-Y1) * b15 * F5*(1-F5) * input1 + (output2-Y2) * b25 * F5*(1-F5) * input1 Dera52    = (output1-Y1) * b15 * F5*(1-F5) * input2 + (output2-Y2) * b25 * F5*(1-F5) * input2 Dera53    = (output1-Y1) * b15 * F5*(1-F5) * input3 + (output2-Y2) * b25 * F5*(1-F5) * input3 Dera54    = (output1-Y1) * b15 * F5*(1-F5) * input4 + (output2-Y2) * b25 * F5*(1-F5) * input4   Dera61    = (output1-Y1) * b16 * F6*(1-F6) * input1 + (output2-Y2) * b26 * F6*(1-F6) * input1 Dera62    = (output1-Y1) * b16 * F6*(1-F6) * input2 + (output2-Y2) * b26 * F6*(1-F6) * input2 Dera63    = (output1-Y1) * b16 * F6*(1-F6) * input3 + (output2-Y2) * b26 * F6*(1-F6) * input3 Dera64    = (output1-Y1) * b16 * F6*(1-F6) * input4 + (output2-Y2) * b26 * F6*(1-F6) * input4   //Implementing BackPropagation Fbias1=Fbias1-ETAi*DerFbias1 Fbias2=Fbias2-ETAi*DerFbias2 Fbias3=Fbias3-ETAi*DerFbias3 Fbias4=Fbias4-ETAi*DerFbias4 Fbias5=Fbias5-ETAi*DerFbias5 Fbias6=Fbias6-ETAi*DerFbias6   a11=a11-ETAi*Dera11 a12=a12-ETAi*Dera12 a13=a13-ETAi*Dera13 a14=a14-ETAi*Dera14   a21=a21-ETAi*Dera21 a22=a22-ETAi*Dera22 a23=a23-ETAi*Dera23 a24=a24-ETAi*Dera24   a31=a31-ETAi*Dera31 a32=a32-ETAi*Dera32 a33=a33-ETAi*Dera33 a34=a34-ETAi*Dera34   a41=a41-ETAi*Dera41 a42=a42-ETAi*Dera42 a43=a43-ETAi*Dera43 a44=a44-ETAi*Dera44   a51=a51-ETAi*Dera51 a52=a52-ETAi*Dera52 a53=a53-ETAi*Dera53 a54=a54-ETAi*Dera54   a61=a61-ETAi*Dera61 a62=a62-ETAi*Dera62 a63=a63-ETAi*Dera63 a64=a64-ETAi*Dera64   //GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64)   NEXT ENDIF ENDIF //ENDIF   ///////////////////    NEW PREDICTION  /////////////////// //    >>> INPUT NEURONS <<< input1=variable1 input2=variable2 input3=variable3 input4=variable4 //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) ENDIF   return output1 coloured(0,150,0) style(line,2) as "prediction long" , output2 coloured(200,0,0) style(line,2) as "prediction short",0.5 coloured(0,0,200) as "0.5", 0.6 coloured(0,0,200) as "0.6", 0.7 coloured(0,0,200) as "0.7", 0.8 coloured(0,0,200) as "0.8"

   

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  noisette

  

  Leo

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  09/02/2018 at 9:27 PM #79595
  GraHal

  Leo wrote:

  Test 100K should be taking ages or ?

  Yes it was taking ages, but I was doing other tasks so – for once – I didn’t mind!

  I wasn’t optimising any variables / hyperparameters  in your code … just my TB and SL.  During the running of 7000 combinations over 100k bars it was if the neural network was self-learning and auto-changing values within your code to give higher overall profit than if I had simply input the optimised value of TP and SL and then pressed Probacktest my System to give a one combination result.

  Maybe I will try the same exercise again tomorrow and post results to prove I am not deluded!? 🙂

  

  GraHal

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  09/03/2018 at 9:11 AM #79615
  GraHal

  I just realised why I might have been confusing you Leo! 🙂

  My two most recent posts above were while I was optimising my Strategy / System (not your Indicator).

  I should have posted on my Systems Topic (I will in future to save confusion).

  If a Mod / Nicolas wants to move – mine #79577, Leos answer,   #79591 and mine #79595 – to my Systems Topic below then feel free?  And then delete this post?

  https://www.prorealcode.com/topic/long-only-trading-with-trend-confirmation-on-5-minutes-timeframe/page/3/

  

  GraHal

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  09/09/2018 at 10:31 PM #80054
  Leo

  Hi all,

  Here another version of the neural network, I improved a bit the back propagation loop.

  I also change the inputs ( it can be what ever you want as long as ETA is calibrated)

  // Hyperparameters to be optimized // ETA=0.05 //known as the learning rate //candlesback=7 // for the classifier //ProfitRiskRatio=2 // for the classifier //spread=0.9 // for the classifier //P1=20 //FOR CURVE AS INPUT //P1=200 //FOR CURVE AS INPUT ///////////////// CLASSIFIER ///////////// myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT ///////////////////////// NEURONAL NETWORK /////////////////// // ...INITIAL VALUES... once a11=1 once a12=1 once a13=1 once a14=1 once a21=1 once a22=1 once a23=1 once a24=1 once a31=1 once a32=1 once a33=1 once a34=1 once a41=1 once a42=1 once a43=1 once a44=1 once a51=1 once a52=1 once a53=1 once a54=1 once a61=1 once a62=1 once a63=1 once a64=1 once Fbias1=0 once Fbias2=0 once Fbias3=0 once Fbias4=0 once Fbias5=0 once Fbias6=0 once b11=1 once b12=1 once b13=1 once b14=1 once b15=1 once b16=1 once b21=1 once b22=1 once b23=1 once b24=1 once b25=1 once b26=1 once Obias1=0 once Obias2=0 // ...DEFINITION OF INPUTS... //ANGLE DEFINITION ONCE PANGLE1=ROUND(SQRT(P1/2)) CURVE1=AVERAGE[P1](CLOSE) ANGLE1=ATAN(CURVE1-CURVE1[1])*180/3.1416 ANGLEAVERAGE1=WeightedAverage[PANGLE1](ANGLE1) ONCE PANGLE2=ROUND(SQRT(P2/2)) CURVE2=AVERAGE[P2](CLOSE) ANGLE2=ATAN(CURVE2-CURVE2[1])*180/3.1416 ANGLEAVERAGE2=WeightedAverage[PANGLE2](ANGLE2) variable1= (close-CURVE1)/CURVE1 *100 //or to be defined variable2= (CURVE1-CURVE2)/CURVE2 *100 //or to be defined variable3= ANGLEAVERAGE1 // to be defined variable4= ANGLEAVERAGE2 // to be defined // >>> LEARNING PROCESS <<< // If the classifier has detected a wining trade in the past //IF hour > 7 and hour < 21 then //STORING THE LEARNING DATA IF classifierlong=1 or classifiershort=1 THEN candleentry0010=candleentry0009 Y10010=Y10009 Y20010=Y20009 candleentry0009=candleentry0008 Y10009=Y10008 Y20009=Y20008 candleentry0008=candleentry0007 Y10008=Y10007 Y20008=Y20007 candleentry0007=candleentry0006 Y10007=Y10006 Y20007=Y20006 candleentry0006=candleentry0005 Y10006=Y10005 Y20006=Y20005 candleentry0005=candleentry0004 Y10005=Y10004 Y20005=Y20004 candleentry0004=candleentry0003 Y10004=Y10003 Y20004=Y20003 candleentry0003=candleentry0002 Y10003=Y10002 Y20003=Y20002 candleentry0002=candleentry0001 Y10002=Y10001 Y20002=Y20001 candleentry0001=max(candleentrylong,candleentryshort) Y10001=classifierlong Y20001=classifiershort ENDIF IF BARINDEX > 1000 THEN IF classifierlong=1 or classifiershort=1 THEN IF hour > 8 and hour < 21 then FOR i=1 to 10 DO // THERE ARE BETTER IDEAS ETAi=ETA*(0.7*i/10+0.3) //Learning Rate IF i = 1 THEN candleentry=candleentry0010 Y1=Y10010 Y2=Y20010 ENDIF IF i = 2 THEN candleentry=candleentry0009 Y1=Y10009 Y2=Y20009 ENDIF IF i = 3 THEN candleentry=candleentry0008 Y1=Y10008 Y2=Y20008 ENDIF IF i = 4 THEN candleentry=candleentry0007 Y1=Y10007 Y2=Y20007 ENDIF IF i = 5 THEN candleentry=candleentry0006 Y1=Y10006 Y2=Y20006 ENDIF IF i = 6 THEN candleentry=candleentry0005 Y1=Y10005 Y2=Y20005 ENDIF IF i = 7 THEN candleentry=candleentry0004 Y1=Y10004 Y2=Y20004 ENDIF IF i = 8 THEN candleentry=candleentry0003 Y1=Y10003 Y2=Y20003 ENDIF IF i = 9 THEN candleentry=candleentry0002 Y1=Y10002 Y2=Y20002 ENDIF IF i = 10 THEN candleentry=candleentry0001 Y1=Y10001 Y2=Y20001 ENDIF // >>> INPUT FOR NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry] // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) // >>> PARTIAL DERIVATES OF COST FUNCTION <<< // ... CROSS-ENTROPY AS COST FUCTION ... // COST = - ( (Y1*LOG(output1)+(1-Y1)*LOG(1-output1) ) - (Y2*LOG(output2)+(1-Y2)*LOG(1-output2) ) DerObias1 = (output1-Y1) * 1 DerObias2 = (output2-Y2) * 1 Derb11 = (output1-Y1) * F1 Derb12 = (output1-Y1) * F2 Derb13 = (output1-Y1) * F3 Derb14 = (output1-Y1) * F4 Derb15 = (output1-Y1) * F5 Derb16 = (output1-Y1) * F6 Derb21 = (output2-Y2) * F1 Derb22 = (output2-Y2) * F2 Derb23 = (output2-Y2) * F3 Derb24 = (output2-Y2) * F4 Derb25 = (output2-Y2) * F5 Derb26 = (output2-Y2) * F6 //Implementing BackPropagation Obias1=Obias1-ETAi*DerObias1 Obias2=Obias2-ETAi*DerObias2 b11=b11-ETAi*Derb11 b12=b12-ETAi*Derb12 b13=b11-ETAi*Derb13 b14=b11-ETAi*Derb14 b15=b11-ETAi*Derb15 b16=b11-ETAi*Derb16 b21=b11-ETAi*Derb21 b22=b12-ETAi*Derb22 b23=b11-ETAi*Derb23 b24=b11-ETAi*Derb24 b25=b11-ETAi*Derb25 b26=b11-ETAi*Derb26 // >>> PARTIAL DERIVATES OF COST FUNCTION (LAYER) <<< DerFbias1 = (output1-Y1) * b11 * F1*(1-F1) * 1 + (output2-Y2) * b21 * F1*(1-F1) * 1 DerFbias2 = (output1-Y1) * b12 * F2*(1-F2) * 1 + (output2-Y2) * b22 * F2*(1-F2) * 1 DerFbias3 = (output1-Y1) * b13 * F3*(1-F3) * 1 + (output2-Y2) * b23 * F3*(1-F3) * 1 DerFbias4 = (output1-Y1) * b14 * F4*(1-F4) * 1 + (output2-Y2) * b24 * F4*(1-F4) * 1 DerFbias5 = (output1-Y1) * b15 * F5*(1-F5) * 1 + (output2-Y2) * b25 * F5*(1-F5) * 1 DerFbias6 = (output1-Y1) * b16 * F6*(1-F6) * 1 + (output2-Y2) * b26 * F6*(1-F6) * 1 Dera11 = (output1-Y1) * b11 * F1*(1-F1) * input1 + (output2-Y2) * b21 * F1*(1-F1) * input1 Dera12 = (output1-Y1) * b11 * F1*(1-F1) * input2 + (output2-Y2) * b21 * F1*(1-F1) * input2 Dera13 = (output1-Y1) * b11 * F1*(1-F1) * input3 + (output2-Y2) * b21 * F1*(1-F1) * input3 Dera14 = (output1-Y1) * b11 * F1*(1-F1) * input4 + (output2-Y2) * b21 * F1*(1-F1) * input4 Dera21 = (output1-Y1) * b12 * F2*(1-F2) * input1 + (output2-Y2) * b22 * F2*(1-F2) * input1 Dera22 = (output1-Y1) * b12 * F2*(1-F2) * input2 + (output2-Y2) * b22 * F2*(1-F2) * input2 Dera23 = (output1-Y1) * b12 * F2*(1-F2) * input3 + (output2-Y2) * b22 * F2*(1-F2) * input3 Dera24 = (output1-Y1) * b12 * F2*(1-F2) * input4 + (output2-Y2) * b22 * F2*(1-F2) * input4 Dera31 = (output1-Y1) * b13 * F3*(1-F3) * input1 + (output2-Y2) * b23 * F3*(1-F3) * input1 Dera32 = (output1-Y1) * b13 * F3*(1-F3) * input2 + (output2-Y2) * b23 * F3*(1-F3) * input2 Dera33 = (output1-Y1) * b13 * F3*(1-F3) * input3 + (output2-Y2) * b23 * F3*(1-F3) * input3 Dera34 = (output1-Y1) * b13 * F3*(1-F3) * input4 + (output2-Y2) * b23 * F3*(1-F3) * input4 Dera41 = (output1-Y1) * b14 * F4*(1-F4) * input1 + (output2-Y2) * b24 * F4*(1-F4) * input1 Dera42 = (output1-Y1) * b14 * F4*(1-F4) * input2 + (output2-Y2) * b24 * F4*(1-F4) * input2 Dera43 = (output1-Y1) * b14 * F4*(1-F4) * input3 + (output2-Y2) * b24 * F4*(1-F4) * input3 Dera44 = (output1-Y1) * b14 * F4*(1-F4) * input4 + (output2-Y2) * b24 * F4*(1-F4) * input4 Dera51 = (output1-Y1) * b15 * F5*(1-F5) * input1 + (output2-Y2) * b25 * F5*(1-F5) * input1 Dera52 = (output1-Y1) * b15 * F5*(1-F5) * input2 + (output2-Y2) * b25 * F5*(1-F5) * input2 Dera53 = (output1-Y1) * b15 * F5*(1-F5) * input3 + (output2-Y2) * b25 * F5*(1-F5) * input3 Dera54 = (output1-Y1) * b15 * F5*(1-F5) * input4 + (output2-Y2) * b25 * F5*(1-F5) * input4 Dera61 = (output1-Y1) * b16 * F6*(1-F6) * input1 + (output2-Y2) * b26 * F6*(1-F6) * input1 Dera62 = (output1-Y1) * b16 * F6*(1-F6) * input2 + (output2-Y2) * b26 * F6*(1-F6) * input2 Dera63 = (output1-Y1) * b16 * F6*(1-F6) * input3 + (output2-Y2) * b26 * F6*(1-F6) * input3 Dera64 = (output1-Y1) * b16 * F6*(1-F6) * input4 + (output2-Y2) * b26 * F6*(1-F6) * input4 //Implementing BackPropagation Fbias1=Fbias1-ETAi*DerFbias1 Fbias2=Fbias2-ETAi*DerFbias2 Fbias3=Fbias3-ETAi*DerFbias3 Fbias4=Fbias4-ETAi*DerFbias4 Fbias5=Fbias5-ETAi*DerFbias5 Fbias6=Fbias6-ETAi*DerFbias6 a11=a11-ETAi*Dera11 a12=a12-ETAi*Dera12 a13=a13-ETAi*Dera13 a14=a14-ETAi*Dera14 a21=a21-ETAi*Dera21 a22=a22-ETAi*Dera22 a23=a23-ETAi*Dera23 a24=a24-ETAi*Dera24 a31=a31-ETAi*Dera31 a32=a32-ETAi*Dera32 a33=a33-ETAi*Dera33 a34=a34-ETAi*Dera34 a41=a41-ETAi*Dera41 a42=a42-ETAi*Dera42 a43=a43-ETAi*Dera43 a44=a44-ETAi*Dera44 a51=a51-ETAi*Dera51 a52=a52-ETAi*Dera52 a53=a53-ETAi*Dera53 a54=a54-ETAi*Dera54 a61=a61-ETAi*Dera61 a62=a62-ETAi*Dera62 a63=a63-ETAi*Dera63 a64=a64-ETAi*Dera64 //GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64) NEXT ENDIF ENDIF //ENDIF /////////////////// NEW PREDICTION /////////////////// // >>> INPUT NEURONS <<< input1=variable1 input2=variable2 input3=variable3 input4=variable4 // >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) // >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) ENDIF return output1 coloured(0,150,0) style(line,2) as "prediction long" , output2 coloured(200,0,0) style(line,2) as "prediction short",0.5 coloured(0,0,200) as "0.5", 0.6 coloured(0,0,200) as "0.6", 0.7 coloured(0,0,200) as "0.7", 0.8 coloured(0,0,200) as "0.8"

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  // Hyperparameters to be optimized   // ETA=0.05 //known as the learning rate //candlesback=7 // for the classifier //ProfitRiskRatio=2 // for the classifier //spread=0.9 // for the classifier //P1=20 //FOR CURVE AS INPUT //P1=200 //FOR CURVE AS INPUT   /////////////////    CLASSIFIER    /////////////   myATR=average[20](range)+std[20](range) ExtraStopLoss=MyATR //ExtraStopLoss=3*spread*pipsize     //for long trades classifierlong=0 FOR scanL=1 to candlesback DO IF classifierlong[scanL]=1 then BREAK ENDIF LongTradeLength=ProfitRiskRatio*(close[scanL]-(low[scanL]-ExtraStopLoss[scanL])) IF close[scanL]+LongTradeLength < high-spread*pipsize then IF lowest[scanL+1](low) > low[scanL]-ExtraStopLoss[scanL]+spread*pipsize then classifierlong=1 candleentrylong=barindex-scanL BREAK ENDIF ENDIF NEXT   //for short trades classifiershort=0 FOR scanS=1 to candlesback DO IF classifiershort[scanS]=1 then BREAK ENDIF ShortTradeLength=ProfitRiskRatio*((high[scanS]-close[scanS])+ExtraStopLoss[scanS]) IF close[scanS]-ShortTradeLength > low+spread*pipsize then IF highest[scanS+1](high) < high[scanS]+ExtraStopLoss[scanS]-spread*pipsize then classifiershort=1 candleentryshort=barindex-scanS BREAK ENDIF ENDIF NEXT   /////////////////////////  NEURONAL NETWORK  ///////////////////   // ...INITIAL VALUES... once a11=1 once a12=1 once a13=1 once a14=1   once a21=1 once a22=1 once a23=1 once a24=1   once a31=1 once a32=1 once a33=1 once a34=1   once a41=1 once a42=1 once a43=1 once a44=1   once a51=1 once a52=1 once a53=1 once a54=1   once a61=1 once a62=1 once a63=1 once a64=1   once Fbias1=0 once Fbias2=0 once Fbias3=0 once Fbias4=0 once Fbias5=0 once Fbias6=0   once b11=1 once b12=1 once b13=1 once b14=1 once b15=1 once b16=1   once b21=1 once b22=1 once b23=1 once b24=1 once b25=1 once b26=1   once Obias1=0 once Obias2=0   // ...DEFINITION OF INPUTS...   //ANGLE DEFINITION ONCE PANGLE1=ROUND(SQRT(P1/2)) CURVE1=AVERAGE[P1](CLOSE) ANGLE1=ATAN(CURVE1-CURVE1[1])*180/3.1416 ANGLEAVERAGE1=WeightedAverage[PANGLE1](ANGLE1) ONCE PANGLE2=ROUND(SQRT(P2/2)) CURVE2=AVERAGE[P2](CLOSE) ANGLE2=ATAN(CURVE2-CURVE2[1])*180/3.1416 ANGLEAVERAGE2=WeightedAverage[PANGLE2](ANGLE2)     variable1= (close-CURVE1)/CURVE1 *100 //or to be defined variable2= (CURVE1-CURVE2)/CURVE2 *100 //or to be defined variable3= ANGLEAVERAGE1  // to be defined variable4= ANGLEAVERAGE2  // to be defined   //   >>>    LEARNING PROCESS  <<< // If the classifier has detected a wining trade in the past //IF hour > 7 and hour < 21 then   //STORING THE LEARNING DATA IF classifierlong=1 or classifiershort=1 THEN candleentry0010=candleentry0009 Y10010=Y10009 Y20010=Y20009 candleentry0009=candleentry0008 Y10009=Y10008 Y20009=Y20008 candleentry0008=candleentry0007 Y10008=Y10007 Y20008=Y20007 candleentry0007=candleentry0006 Y10007=Y10006 Y20007=Y20006 candleentry0006=candleentry0005 Y10006=Y10005 Y20006=Y20005 candleentry0005=candleentry0004 Y10005=Y10004 Y20005=Y20004 candleentry0004=candleentry0003 Y10004=Y10003 Y20004=Y20003 candleentry0003=candleentry0002 Y10003=Y10002 Y20003=Y20002 candleentry0002=candleentry0001 Y10002=Y10001 Y20002=Y20001 candleentry0001=max(candleentrylong,candleentryshort) Y10001=classifierlong Y20001=classifiershort ENDIF   IF BARINDEX > 1000 THEN IF classifierlong=1 or classifiershort=1 THEN IF hour > 8 and hour < 21 then FOR i=1 to 10 DO //  THERE ARE BETTER IDEAS ETAi=ETA*(0.7*i/10+0.3) //Learning Rate IF i = 1 THEN candleentry=candleentry0010 Y1=Y10010 Y2=Y20010 ENDIF IF i = 2 THEN candleentry=candleentry0009 Y1=Y10009 Y2=Y20009 ENDIF   IF i = 3 THEN candleentry=candleentry0008 Y1=Y10008 Y2=Y20008 ENDIF   IF i = 4 THEN candleentry=candleentry0007 Y1=Y10007 Y2=Y20007 ENDIF IF i = 5 THEN candleentry=candleentry0006 Y1=Y10006 Y2=Y20006 ENDIF IF i = 6 THEN candleentry=candleentry0005 Y1=Y10005 Y2=Y20005 ENDIF IF i = 7 THEN candleentry=candleentry0004 Y1=Y10004 Y2=Y20004 ENDIF IF i = 8 THEN candleentry=candleentry0003 Y1=Y10003 Y2=Y20003 ENDIF IF i = 9 THEN candleentry=candleentry0002 Y1=Y10002 Y2=Y20002 ENDIF IF i = 10 THEN candleentry=candleentry0001 Y1=Y10001 Y2=Y20001 ENDIF   //    >>> INPUT FOR NEURONS <<< input1=variable1[barindex-candleentry] input2=variable2[barindex-candleentry] input3=variable3[barindex-candleentry] input4=variable4[barindex-candleentry]   //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2))   //   >>> PARTIAL DERIVATES OF COST FUNCTION <<< //     ... CROSS-ENTROPY AS COST FUCTION ... // COST = - ( (Y1*LOG(output1)+(1-Y1)*LOG(1-output1) ) - (Y2*LOG(output2)+(1-Y2)*LOG(1-output2) )   DerObias1 = (output1-Y1) * 1 DerObias2 = (output2-Y2) * 1   Derb11    = (output1-Y1) * F1 Derb12    = (output1-Y1) * F2 Derb13    = (output1-Y1) * F3 Derb14    = (output1-Y1) * F4 Derb15    = (output1-Y1) * F5 Derb16    = (output1-Y1) * F6   Derb21    = (output2-Y2) * F1 Derb22    = (output2-Y2) * F2 Derb23    = (output2-Y2) * F3 Derb24    = (output2-Y2) * F4 Derb25    = (output2-Y2) * F5 Derb26    = (output2-Y2) * F6   //Implementing BackPropagation Obias1=Obias1-ETAi*DerObias1 Obias2=Obias2-ETAi*DerObias2   b11=b11-ETAi*Derb11 b12=b12-ETAi*Derb12 b13=b11-ETAi*Derb13 b14=b11-ETAi*Derb14 b15=b11-ETAi*Derb15 b16=b11-ETAi*Derb16   b21=b11-ETAi*Derb21 b22=b12-ETAi*Derb22 b23=b11-ETAi*Derb23 b24=b11-ETAi*Derb24 b25=b11-ETAi*Derb25 b26=b11-ETAi*Derb26   //   >>> PARTIAL DERIVATES OF COST FUNCTION (LAYER) <<<   DerFbias1 = (output1-Y1) * b11 * F1*(1-F1) * 1 + (output2-Y2) * b21 * F1*(1-F1) * 1 DerFbias2 = (output1-Y1) * b12 * F2*(1-F2) * 1 + (output2-Y2) * b22 * F2*(1-F2) * 1 DerFbias3 = (output1-Y1) * b13 * F3*(1-F3) * 1 + (output2-Y2) * b23 * F3*(1-F3) * 1 DerFbias4 = (output1-Y1) * b14 * F4*(1-F4) * 1 + (output2-Y2) * b24 * F4*(1-F4) * 1 DerFbias5 = (output1-Y1) * b15 * F5*(1-F5) * 1 + (output2-Y2) * b25 * F5*(1-F5) * 1 DerFbias6 = (output1-Y1) * b16 * F6*(1-F6) * 1 + (output2-Y2) * b26 * F6*(1-F6) * 1   Dera11    = (output1-Y1) * b11 * F1*(1-F1) * input1 + (output2-Y2) * b21 * F1*(1-F1) * input1 Dera12    = (output1-Y1) * b11 * F1*(1-F1) * input2 + (output2-Y2) * b21 * F1*(1-F1) * input2 Dera13    = (output1-Y1) * b11 * F1*(1-F1) * input3 + (output2-Y2) * b21 * F1*(1-F1) * input3 Dera14    = (output1-Y1) * b11 * F1*(1-F1) * input4 + (output2-Y2) * b21 * F1*(1-F1) * input4   Dera21    = (output1-Y1) * b12 * F2*(1-F2) * input1 + (output2-Y2) * b22 * F2*(1-F2) * input1 Dera22    = (output1-Y1) * b12 * F2*(1-F2) * input2 + (output2-Y2) * b22 * F2*(1-F2) * input2 Dera23    = (output1-Y1) * b12 * F2*(1-F2) * input3 + (output2-Y2) * b22 * F2*(1-F2) * input3 Dera24    = (output1-Y1) * b12 * F2*(1-F2) * input4 + (output2-Y2) * b22 * F2*(1-F2) * input4   Dera31    = (output1-Y1) * b13 * F3*(1-F3) * input1 + (output2-Y2) * b23 * F3*(1-F3) * input1 Dera32    = (output1-Y1) * b13 * F3*(1-F3) * input2 + (output2-Y2) * b23 * F3*(1-F3) * input2 Dera33    = (output1-Y1) * b13 * F3*(1-F3) * input3 + (output2-Y2) * b23 * F3*(1-F3) * input3 Dera34    = (output1-Y1) * b13 * F3*(1-F3) * input4 + (output2-Y2) * b23 * F3*(1-F3) * input4   Dera41    = (output1-Y1) * b14 * F4*(1-F4) * input1 + (output2-Y2) * b24 * F4*(1-F4) * input1 Dera42    = (output1-Y1) * b14 * F4*(1-F4) * input2 + (output2-Y2) * b24 * F4*(1-F4) * input2 Dera43    = (output1-Y1) * b14 * F4*(1-F4) * input3 + (output2-Y2) * b24 * F4*(1-F4) * input3 Dera44    = (output1-Y1) * b14 * F4*(1-F4) * input4 + (output2-Y2) * b24 * F4*(1-F4) * input4   Dera51    = (output1-Y1) * b15 * F5*(1-F5) * input1 + (output2-Y2) * b25 * F5*(1-F5) * input1 Dera52    = (output1-Y1) * b15 * F5*(1-F5) * input2 + (output2-Y2) * b25 * F5*(1-F5) * input2 Dera53    = (output1-Y1) * b15 * F5*(1-F5) * input3 + (output2-Y2) * b25 * F5*(1-F5) * input3 Dera54    = (output1-Y1) * b15 * F5*(1-F5) * input4 + (output2-Y2) * b25 * F5*(1-F5) * input4   Dera61    = (output1-Y1) * b16 * F6*(1-F6) * input1 + (output2-Y2) * b26 * F6*(1-F6) * input1 Dera62    = (output1-Y1) * b16 * F6*(1-F6) * input2 + (output2-Y2) * b26 * F6*(1-F6) * input2 Dera63    = (output1-Y1) * b16 * F6*(1-F6) * input3 + (output2-Y2) * b26 * F6*(1-F6) * input3 Dera64    = (output1-Y1) * b16 * F6*(1-F6) * input4 + (output2-Y2) * b26 * F6*(1-F6) * input4   //Implementing BackPropagation Fbias1=Fbias1-ETAi*DerFbias1 Fbias2=Fbias2-ETAi*DerFbias2 Fbias3=Fbias3-ETAi*DerFbias3 Fbias4=Fbias4-ETAi*DerFbias4 Fbias5=Fbias5-ETAi*DerFbias5 Fbias6=Fbias6-ETAi*DerFbias6   a11=a11-ETAi*Dera11 a12=a12-ETAi*Dera12 a13=a13-ETAi*Dera13 a14=a14-ETAi*Dera14   a21=a21-ETAi*Dera21 a22=a22-ETAi*Dera22 a23=a23-ETAi*Dera23 a24=a24-ETAi*Dera24   a31=a31-ETAi*Dera31 a32=a32-ETAi*Dera32 a33=a33-ETAi*Dera33 a34=a34-ETAi*Dera34   a41=a41-ETAi*Dera41 a42=a42-ETAi*Dera42 a43=a43-ETAi*Dera43 a44=a44-ETAi*Dera44   a51=a51-ETAi*Dera51 a52=a52-ETAi*Dera52 a53=a53-ETAi*Dera53 a54=a54-ETAi*Dera54   a61=a61-ETAi*Dera61 a62=a62-ETAi*Dera62 a63=a63-ETAi*Dera63 a64=a64-ETAi*Dera64   //GradientNorm = SQRT(DerObias1*DerObias1 + DerObias2*DerObias2+Derb11*Derb11+Derb12*Derb12+Derb13*Derb13+Derb14*Derb14+Derb15*Derb15+Derb16*Derb16 + Derb21*Derb21+Derb22*Derb22+Derb23*Derb23+Derb24*Derb24+Derb25*Derb25+Derb26*Derb26 + DerFbias1*DerFbias1+DerFbias2*DerFbias2+DerFbias3+DerFbias3+DerFbias4*DerFbias4+DerFbias4*DerFbias5+DerFbias6*DerFbias6 + Dera11*Dera11+Dera12*Dera12+Dera13*Dera13+Dera14*Dera14 + Dera21*Dera21+Dera22*Dera22+Dera23*Dera23+Dera24*Dera24 + Dera31*Dera31+Dera32*Dera32+Dera33*Dera33+Dera34*Dera34 + Dera41*Dera41+Dera42*Dera42+Dera43*Dera43+Dera44*Dera44 + Dera51*Dera51+Dera52*Dera52+Dera53*Dera53+Dera54*Dera54 + Dera61*Dera61+Dera62*Dera62+Dera63*Dera63+Dera64*Dera64)   NEXT ENDIF ENDIF //ENDIF   ///////////////////    NEW PREDICTION  /////////////////// //    >>> INPUT NEURONS <<< input1=variable1 input2=variable2 input3=variable3 input4=variable4 //   >>> FIRST LAYER OF NEURONS <<< F1=a11*input1+a12*input2+a13*input3+a14*input4+Fbias1 F2=a21*input1+a22*input2+a23*input3+a24*input4+Fbias2 F3=a31*input1+a32*input2+a33*input3+a34*input4+Fbias3 F4=a41*input1+a42*input2+a43*input3+a44*input4+Fbias4 F5=a51*input1+a52*input2+a53*input3+a54*input4+Fbias5 F6=a61*input1+a62*input2+a63*input3+a64*input4+Fbias6 F1=1/(1+EXP(-1*F1)) F2=1/(1+EXP(-1*F2)) F3=1/(1+EXP(-1*F3)) F4=1/(1+EXP(-1*F4)) F5=1/(1+EXP(-1*F5)) F6=1/(1+EXP(-1*F6)) //   >>> OUTPUT NEURONS <<< output1=b11*F1+b12*F2+b13*F3+b14*F4+b15*F5+b16*F6+Obias1 output2=b21*F1+b22*F2+b23*F3+b24*F4+b25*F5+b26*F6+Obias2 output1=1/(1+EXP(-1*output1)) output2=1/(1+EXP(-1*output2)) ENDIF   return output1 coloured(0,150,0) style(line,2) as "prediction long" , output2 coloured(200,0,0) style(line,2) as "prediction short",0.5 coloured(0,0,200) as "0.5", 0.6 coloured(0,0,200) as "0.6", 0.7 coloured(0,0,200) as "0.7", 0.8 coloured(0,0,200) as "0.8"

   

  

  Leo

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  09/11/2018 at 7:39 PM #80200
  GraHal

  Leo wrote:

  P1=200 //FOR CURVE AS INPUT

  Leo, should Line 8 be …

  P2=200 //FOR CURVE AS INPUT

  1	P2=200 //FOR CURVE AS INPUT

   

  

  GraHal

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  09/11/2018 at 8:37 PM #80206
  GraHal

  GraHal wrote:

  My two most recent posts above were while I was optimising my Strategy / System (not your Indicator).

  Here is an example with screen shots. I feel like I’m losing my grip ! ha

  If I optimise P1 and P2 then the top result (values 38 and 100) is Leo 18.

  If I then insert values 38 and 100 into my System I get result Leo 19.

  Anybody any suggestions or comments, esp Leo?

  Edit / PS

  Don’t worry just yet … I may have the reason, more later! 🙂

  

  GraHal

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  09/11/2018 at 9:14 PM #80211
  GraHal

  I’ve got into the habit of naming my variables A+ Line number,  so for Line 8 (Line 11 in my System) I used …

     P2=A11 //FOR CURVE AS INPUT

  1	P2=A11 //FOR CURVE AS INPUT

   

  But there is also an A11 in Leo’s code and so I was making P2 = A11 (by mistake) but it produced great results!! 🙂

  Leo’s Neural Network code is beyond me, but I will try and explore what is going on re my blunder! 🙂

  Also I will set my corrupted System going on Demo Forward Test and report results over on my own Topic using Leo’s Neural Network code.

  So in summary … a storm in a tea cup, but I may have unearthed something interesting?

   

  

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  09/12/2018 at 3:56 AM #80219
  Leo

  I read you fisrt code and I were thinking on that, then my daugther finally slept againg (4:33 am) and cotinue reading.

  You take one weight A11 and reset it every time with a value 38. Do not feel disappointed, I do not know either what effect take it that particular neuron and propagate to the others. I can imagine the effect was like drinking alcohol to that neuron. Haha

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  09/30/2018 at 12:29 PM #81619
  Leo

  Awesome Video about the Math of neural networks. It is what I am coding here.

  https://youtu.be/N4gDikiec8E

   

  

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  09/30/2018 at 9:17 PM #81637
  didi059

  Leo

  There is software published by Google allowing Deep Learning . “Tensorflow”

  In the following presentation , you will find interesting topics

  https://youtu.be/vq2nnJ4g6N0

  In the mean time I made already a list of additional data that  be included in the system:

  Ret : daily return of the asset
  HV : realized volatility for the past 5 sessions
  M5 Momentum 5
  M10 Momentum 10
  VIX9d or formally VXST 9-day volatility index of the S&P 500 (= market sentiment short term)
  VIX of S&P 500   (= market sentiment long term)
  VVIX  (= market sentiment momentum)
  MO month of the year (seasonality)
  DAY day of the week (seasonality)

   

  

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  10/01/2018 at 5:38 AM #81642
  Leo

  Hi didi059,

  there are other libraries for machine learning like sciki-learn but we need to learn another language of programming and  other interface with the brocker. So far I do not know how to implement for implement those libraries. We know from the beginning that prorealtime is not the right language for artificial intelligent.

  Those indicator sound good, I keep them in mind.

  

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  10/01/2018 at 4:21 PM #81685
  Meta Signals Pro

  Hi Leo,

  Any idea of I should do to make it universaly work with stocks?

  Best,

  Chris

  

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  10/02/2018 at 7:14 AM #81723
  Leo

  Hi, Actually is already universal, If it is working that the issue 🙂

  From line 106 to 123 you add your indicators or variable and the output is a prediction of going long or short base on those variable you choose

  Actuallly in an strategy, those variables ca be in other time frames

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  Andyswede, Meta Signals Pro

  

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  10/06/2018 at 2:10 PM #82157
  noisette

  Hi,

  thanks for the code.

  If I’m not wrong you use “if barindex > xxxx then” to create a learning period.

  Do you think that if I replace BARINDEX by “DEFPARAM PRELOADBARS = xxx” it could allow to create a learning period a start to trade immediately?

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  noisette

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  10/15/2018 at 4:05 AM #82757
  Leo

  Yes you are right.

  I use that for show it as an indicator. Defparam bars are also used for learning process.

  I would let “if barindex > xxxx then” feature inside because there are so much values involve that better have time for load everything.

  

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